An Empirical Evaluation of Mediation Effect Analysis With Manifest and Latent Variables Using Markov Chain Monte Carlo and Alternative Estimation Methods

Abstract

AbstractRecently, the Markov chain Monte Carlo (MCMC) estimation method has become explosively popular in a variety of quantitative research methods. In mediation effect analysis (MEA), the MCMC estimation methods can be a promising tool and an important alternative as compared with traditional methods (e.g., the z test using the delta method and the bias-corrected bootstrapping method) in addressing issues such as nonconvergence and complex modeling. In this article, a subject-level MCMC approach for the single MEA is empirically evaluated and compared with traditional methods through Monte Carlo simulation. The evaluation covers point and interval estimates of both manifest and latent variables across conditions including sample size, effect size, and magnitude of factor loadings. BUGS codes for MEA with both manifest and latent variables are provided that can be easily adapted to fit various MEA models in practice. Keywords: Bayesian inferencelatent variablesmanifest variablesMarkov chain Monte Carlomediation effect